


import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()



class 				A       (			UpperCAmelCase__					,    UpperCAmelCase__					,    UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					StableUnCLIPPipeline
					A__     =					TEXT_TO_IMAGE_PARAMS
					A__     =					TEXT_TO_IMAGE_BATCH_PARAMS
					A__     =					TEXT_TO_IMAGE_IMAGE_PARAMS
					A__     =					TEXT_TO_IMAGE_IMAGE_PARAMS

					# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
					A__     =					False

					def     lowerCamelCase__	(self       :		Any						)		->  str:




							"""simple docstring"""


							lowercase__	     =						32
							lowercase__	     =						embedder_hidden_size

							# prior components

							torch.manual_seed(0						)
							lowercase__	     =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

							torch.manual_seed(0						)
							lowercase__	     =						CLIPTextModelWithProjection(
							    CLIPTextConfig(
							        bos_token_id=0 ,   eos_token_id=2 ,   hidden_size=_UpperCAmelCase ,   projection_dim=_UpperCAmelCase ,   intermediate_size=37 ,   layer_norm_eps=1E-0_5 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   pad_token_id=1 ,   vocab_size=1000 ,   )						)

							torch.manual_seed(0						)
							lowercase__	     =						PriorTransformer(
							    num_attention_heads=2 ,   attention_head_dim=12 ,   embedding_dim=_UpperCAmelCase ,   num_layers=1 ,   )

							torch.manual_seed(0						)
							lowercase__	     =						DDPMScheduler(
							    variance_type="""fixed_small_log""" ,   prediction_type="""sample""" ,   num_train_timesteps=1000 ,   clip_sample=_UpperCAmelCase ,   clip_sample_range=5.0 ,   beta_schedule="""squaredcos_cap_v2""" ,   )

							# regular denoising components

							torch.manual_seed(0						)
							lowercase__	     =						StableUnCLIPImageNormalizer(embedding_dim=_UpperCAmelCase						)
							lowercase__	     =						DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""						)

							torch.manual_seed(0						)
							lowercase__	     =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

							torch.manual_seed(0						)
							lowercase__	     =						CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0 ,   eos_token_id=2 ,   hidden_size=_UpperCAmelCase ,   projection_dim=32 ,   intermediate_size=37 ,   layer_norm_eps=1E-0_5 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   pad_token_id=1 ,   vocab_size=1000 ,   )						)

							torch.manual_seed(0						)
							lowercase__	     =						UNetaDConditionModel(
							    sample_size=32 ,   in_channels=4 ,   out_channels=4 ,   down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""") ,   up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""") ,   block_out_channels=(32, 64) ,   attention_head_dim=(2, 4) ,   class_embed_type="""projection""" ,   projection_class_embeddings_input_dim=embedder_projection_dim * 2 ,   cross_attention_dim=_UpperCAmelCase ,   layers_per_block=1 ,   upcast_attention=_UpperCAmelCase ,   use_linear_projection=_UpperCAmelCase ,   )

							torch.manual_seed(0						)
							lowercase__	     =						DDIMScheduler(
							    beta_schedule="""scaled_linear""" ,   beta_start=0.00_085 ,   beta_end=0.012 ,   prediction_type="""v_prediction""" ,   set_alpha_to_one=_UpperCAmelCase ,   steps_offset=1 ,   )

							torch.manual_seed(0						)
							lowercase__	     =						AutoencoderKL()

							lowercase__	     =						{
							    # prior components
							    """prior_tokenizer""": prior_tokenizer,
							    """prior_text_encoder""": prior_text_encoder,
							    """prior""": prior,
							    """prior_scheduler""": prior_scheduler,
							    # image noising components
							    """image_normalizer""": image_normalizer,
							    """image_noising_scheduler""": image_noising_scheduler,
							    # regular denoising components
							    """tokenizer""": tokenizer,
							    """text_encoder""": text_encoder,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							}

							return components

					def     lowerCamelCase__	(self       :		Dict ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		List[str]=0						)		->  List[str]:




							"""simple docstring"""


							if str(_UpperCAmelCase						).startswith("""mps"""						):
									lowercase__	     =						torch.manual_seed(_UpperCAmelCase						)
							else:
									lowercase__	     =						torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
							lowercase__	     =						{
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """prior_num_inference_steps""": 2,
							    """output_type""": """numpy""",
							}
							return inputs

					def     lowerCamelCase__	(self       :		List[str]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						torch_device == """cpu"""

							self._test_attention_slicing_forward_pass(test_max_difference=_UpperCAmelCase						)


					def     lowerCamelCase__	(self       :		List[Any]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						torch_device in ["""cpu""", """mps"""]

							self._test_inference_batch_single_identical(test_max_difference=_UpperCAmelCase						)






@slow
@require_torch_gpu
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		Dict						)		->  Optional[int]:




							"""simple docstring"""


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""						)

							lowercase__	     =						StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""" ,   torch_dtype=torch.floataa						)
							pipe.to(_UpperCAmelCase						)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowercase__	     =						torch.Generator(device="""cpu"""						).manual_seed(0						)
							lowercase__	     =						pipe("""anime turle""" ,   generator=_UpperCAmelCase ,   output_type="""np"""						)

							lowercase__	     =						output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(_UpperCAmelCase ,   _UpperCAmelCase						)


					def     lowerCamelCase__	(self       :		Any						)		->  Optional[Any]:




							"""simple docstring"""


							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowercase__	     =						StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""" ,   torch_dtype=torch.floataa						)
							lowercase__	     =						pipe.to(_UpperCAmelCase						)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowercase__	     =						pipe(
							    """anime turtle""" ,   prior_num_inference_steps=2 ,   num_inference_steps=2 ,   output_type="""np""" ,   )

							lowercase__	     =						torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9


"""simple docstring"""


from __future__ import annotations

from typing import Any






class   __UpperCAmelCase		:

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase = 0 ):
										lowerCamelCase__   , lowerCamelCase__   =row, column
										lowerCamelCase__   =[[default_value for c in range(_lowerCamelCase )] for r in range(_lowerCamelCase )]

				def __str__( self ):
										lowerCamelCase__   =F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

										# Make string identifier
										lowerCamelCase__   =0
										for row_vector in self.array:
																for obj in row_vector:
																						lowerCamelCase__   =max(_lowerCamelCase      ,					len(str(_lowerCamelCase ) ) )
										lowerCamelCase__   =F'''%{max_element_length}s'''

										# Make string and return
										def single_line(_lowerCamelCase ) -> str:
																nonlocal string_format_identifier
																lowerCamelCase__   ="["
																line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
																line += "]"
																return line

										s += "\n".join(single_line(_lowerCamelCase ) for row_vector in self.array )
										return s

				def __repr__( self ):
										return str(self )

				def 						_a      ( self      ,					_lowerCamelCase ):
										if not (isinstance(_lowerCamelCase      ,					(list, tuple) ) and len(_lowerCamelCase ) == 2):
																return False
										elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																return False
										else:
																return True

				def __getitem__( self      ,					_lowerCamelCase ):
										assert self.validate_indicies(_lowerCamelCase )
										return self.array[loc[0]][loc[1]]

				def __setitem__( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										assert self.validate_indicies(_lowerCamelCase )
										lowerCamelCase__   =value

				def __add__( self      ,					_lowerCamelCase ):
										assert isinstance(_lowerCamelCase      ,					_lowerCamelCase )
										assert self.row == another.row and self.column == another.column

										# Add
										lowerCamelCase__   =Matrix(self.row      ,					self.column )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =self[r, c] + another[r, c]
										return result

				def __neg__( self ):
										lowerCamelCase__   =Matrix(self.row      ,					self.column )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =-self[r, c]
										return result

				def __sub__( self      ,					_lowerCamelCase ):
										return self + (-another)

				def __mul__( self      ,					_lowerCamelCase ):

										if isinstance(_lowerCamelCase      ,					(int, float) ):  # Scalar multiplication
																lowerCamelCase__   =Matrix(self.row      ,					self.column )
																for r in range(self.row ):
																						for c in range(self.column ):
																												lowerCamelCase__   =self[r, c] * another
																return result
										elif isinstance(_lowerCamelCase      ,					_lowerCamelCase ):  # Matrix multiplication
																assert self.column == another.row
																lowerCamelCase__   =Matrix(self.row      ,					another.column )
																for r in range(self.row ):
																						for c in range(another.column ):
																												for i in range(self.column ):
																																		result[r, c] += self[r, i] * another[i, c]
																return result
										else:
																lowerCamelCase__   =F'''Unsupported type given for another ({type(_lowerCamelCase )})'''
																raise TypeError(_lowerCamelCase )

				def 						_a      ( self ):
										lowerCamelCase__   =Matrix(self.column      ,					self.row )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =self[r, c]
										return result






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										assert isinstance(_lowerCamelCase      ,					_lowerCamelCase ) and isinstance(_lowerCamelCase      ,					_lowerCamelCase )
										assert self.row == self.column == u.row == v.row  # u, v should be column vector
										assert u.column == v.column == 1  # u, v should be column vector

										# Calculate
										lowerCamelCase__   =v.transpose()
										lowerCamelCase__   =(v_t * self * u)[0, 0] + 1
										if numerator_factor == 0:
																return None  # It's not invertable
										return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





	def      lowerCamelCase_					(   )				->   None:




							'''simple docstring'''



							lowerCamelCase__   =Matrix(3  ,       3  ,       0							)
							for i in range(3							):
													lowerCamelCase__   =1
							print(F'''a^(-1) is {ainv}'''							)
							# u, v
							lowerCamelCase__   =Matrix(3  ,       1  ,       0							)
							lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =1, 2, -3
							lowerCamelCase__   =Matrix(3  ,       1  ,       0							)
							lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =4, -2, 5
							print(F'''u is {u}'''							)
							print(F'''v is {v}'''							)
							print(F'''uv^T is {u * v.transpose()}'''							)
							# Sherman Morrison
							print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowerCAmelCase  ,       __lowerCAmelCase							)}'''							)





	def      lowerCamelCase_					(   )				->   None:




							'''simple docstring'''



							import doctest

							doctest.testmod()





	testa()


